A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors

Abstract

In this work, we introduce a novel approach to model the rain and fog effect on the light detection and ranging (LiDAR) sensor performance for the simulation-based testing of LiDAR systems. The proposed methodology allows for the simulation of the rain and fog effect using the rigorous applications of the Mie scattering theory on the time domain for transient and point cloud levels for spatial analyses. The time domain analysis permits us to benchmark the virtual LiDAR signal attenuation and signal-to-noise ratio (SNR) caused by rain and fog droplets. In addition, the detection rate (DR), false detection rate (FDR), and distance error derror of the virtual LiDAR sensor due to rain and fog droplets are evaluated on the point cloud level. The mean absolute percentage error (MAPE) is used to quantify the simulation and real measurement results on the time domain and point cloud levels for the rain and fog droplets. The results of the simulation and real measurements match well on the time domain and point cloud levels if the simulated and real rain distributions are the same. The real and virtual LiDAR sensor performance degrades more under the influence of fog droplets than in rain.

LiMOX—A Point Cloud Lidar Model Toolbox Based on NVIDIA OptiX Ray Tracing Engine

“…A further analysis and derivation of atmospheric conditions and weather effects and their impact on the range–reflectivity limits can be found in [ 38 ]. Another detailed analysis of weather effects from rain and fog and their respective simulation results are shown in [ 39 ].…”

Assessment of Lidar Point Cloud Simulation Using Phenomenological Range-Reflectivity Limits for Feature Validation

“…A full-scale analysis of adverse weather conditions for a particular lidar sensor (Cube 1 by Blickfeld) is demonstrated in [26] by applying Mie scattering theory. Haider et al investigate the signal-to-noise ratio under attenuation conditions, as well as detection rates, false positives, and distance errors caused by rain and fog.…”

“…A generic attenuation model for lidar is presented in the following. It is a phenomenological model based on heuristic parameters, as opposed to rigorous physical derivations [26], such as Mie scattering theory. An advantage of the model is that it is applicable without detailed knowledge of physical parameters of the weather condition, e.g., droplet size distributions and intensities, or the sensor specifics.…”